جست‌وجوی استاد و پژوهشگر

Andrew Titmanمشاهده پروفایل

استاد

• Survival Analysis
• Multi-State Modeling
• Medical Statistics
• +۳ مورد دیگر

Andrew Titman is a Professor in the School of Mathematical Sciences at Lancaster University . His research focuses on survival analysis and multi-state modeling with applications in medical statistics and quantitative methodology. Primary Affiliation: School of Mathematical Sciences, Lancaster University Contact: a.titman@lancaster.ac.uk Research Interests include: Development of non-Markov multi-state models for complex survival data Latent variable modeling for health-related quality of life questionnaires Methodology for informative observation times in clinical trials Health economic decision modeling applications Survival analysis with misclassification and time non-homogeneity Recent Publications demonstrate expertise in: Bayesian state-space models for sports analytics Multi-state model diagnostics and goodness-of-fit testing Medical applications in endocrinology and hematology Simulation methodology with input uncertainty quantification Scientific Contributions include: Development of R packages: nhm, CIFresolve, miscIC Creation of the MarkovTest GitHub repository for multi-state model validation Methodological frameworks for non-homogeneous Markov models

Erik Bekkersمشاهده پروفایل

دانشیار

• Geometric Machine Learning
• Deep Learning
• Medical Image Analysis
• +۴ مورد دیگر

Erik Bekkers is an Associate Professor at the University of Amsterdam's Informatics Institute, leading research in the Machine Learning Lab (AMLab). His work bridges geometric mathematics and machine learning, focusing on developing robust and efficient deep learning architectures grounded in symmetry, equivariance, and physical principles. Education: PhD in Biomedical Engineering (cum laude) from Eindhoven University of Technology Previous Roles: Postdoctoral researcher in applied differential geometry at TU/e Department of Applied Mathematics His research spans: Group convolutional neural networks Symmetry-preserving representation learning Generative modeling on manifolds Physics-informed neural networks Medical imaging applications Recent publications emphasize geometric latent variable models, equivariant diffusion methods, and applications to molecular generation, medical imaging, and physics-driven AI. His team actively explores structure-preserving and self-supervised learning techniques. Scientific Awards MICCAI Young Scientist Award (2018) Philips Impact Award (MIDL 2018) NWO VENI grant: Context-Aware AI in Medical Imaging (2023) NWO VIDI grant: Neural Ideograms - Geometry-Grounded AI (2024) As co-founder of the ICML'24 GRaM workshop , he promotes geometry-grounded approaches in AI. His lab actively investigates geometric regularization, manifold-based PDE forecasting, and symmetry-aware generative methods.

Artur Czumajمشاهده پروفایل

استاد

• Theoretical Computer Science
• Randomized Algorithms
• Parallel and Distributed Computing
• +۴ مورد دیگر

Artur Czumaj is a Professor of Computer Science and Director of the Center for Discrete Mathematics and its Applications (DIMAP) at the University of Warwick, United Kingdom. He is a member of the Global Faculty at the University of Cologne and serves as President of the European Association for Theoretical Computer Science (EATCS). PhD and Habilitation from University of Paderborn Recipient of the IBM Award and Fellow of EATCS His research focuses on theoretical computer science, particularly in the design of randomized algorithms, analysis of large data, and applications to parallel/distributed computing, property testing, sublinear algorithms, optimization algorithms, and algorithmic game theory. His work has been funded by major institutions including EPSRC, NSF, Royal Society, European Union, Simons Foundation, and IBM. The 15 most recent publications reflect expertise in graph algorithms, distributed systems, approximation techniques, and algorithmic complexity. His research spans foundational algorithm design to practical applications in large-scale data processing. Fellow of the European Association for Theoretical Computer Science (EATCS) IBM Award recipient Czumaj has held leadership roles in prominent conferences, including Program Committee Chair for STOC, ICALP, SODA, and HALG. His academic career is supported by grants from multiple international research councils and organizations.

Corinna Brungsمشاهده پروفایل

پژوهشگر

• Phytochemistry
• Biodiscovery
• Mass Spectrometry
• +۴ مورد دیگر

Corinna Brungs is a Senior Scientist at the University of Vienna , affiliated with the Faculty of Life Sciences and the Department of Pharmaceutical Sciences (Division of Pharmacognosy). Her research focuses on applying cutting-edge mass spectrometry techniques for natural product identification, biomarker discovery, and untargeted metabolomics workflows. Developing open mass spectral libraries Advancing data processing strategies Specializing in tattoo pigment and metabolite analysis Research Highlights: Brungs bridges phytochemistry and computational metabolomics through method development in mass spectrometry. Her work includes creating standardized protocols for: Tattoo pigment identification In-source fragmentation analysis Non-targeted drug exposure profiling Integration of mobility-resolved spectrometry Scientific Awards: 2025 Early Career Prize (Metabolomics Society) 2025 Early Career Travel Award (Metabolomics Society) 2022 Czech Academy of Sciences Fellowship Publications (2022-2025) demonstrate leadership in: MALDI-TIMS-MS2 bioimaging MZmine 3 software development Metabolomics data reproducibility Mass spectral library matching

Marco Caliendoمشاهده پروفایل

استاد

• Labor Market Policies
• Entrepreneurship
• Personality Traits and Economic Outcomes
• +۳ مورد دیگر

Prof. Marco Caliendo is a Professor of Empirical Economics at the University of Potsdam and a faculty member of the Berlin School of Economics (BSoE). He holds a PhD from Goethe University Frankfurt and is a Research Fellow at IZA, DIW Berlin, and IAB Nuremberg. His research focuses on labor market policies, entrepreneurship, personality traits' economic impacts, and microeconometrics. Education: Bachelor/Master in Economics at University of Manchester and Goethe University Frankfurt PhD in Economics (April 2005), Goethe University Frankfurt Key Roles: Director of Research at IZA (2009–2011) Program Director for 'Evaluation of Labor Market Programs' at IZA (2011–2023) Speaker of the Center for Economic Policy Analysis (CEPA) His research interests include labor market program evaluations, self-employment dynamics, unemployment behavior, and the role of personality in economic decisions. He has led projects like 'Evaluation of Startchancen Program' and studies on minimum wage impacts. His work emphasizes policy relevance, with over 21,000 Google Scholar citations (h-index 54) and top global rankings in RePEc. Publications span journals such as Review of Economics and Statistics and Journal of Human Resources , focusing on labor market interventions, entrepreneurship, and behavioral economics. His recent work addresses pandemic effects on self-employed mental health, minimum wage reforms, and start-up subsidy outcomes. Prof. Caliendo advises PhD students through workshops (e.g., IZA Entrepreneurship Research) and collaborates with institutions like CEPA and PCQR. His lab coordinates research on human capital investments and policy evaluation.

• Fuzzy Logic
• Artificial Intelligence
• Neural Networks
• +۶ مورد دیگر

Professor Mahdi Mahfouf holds a Chair in the School of Electrical and Electronic Engineering at the University of Sheffield. He has held academic roles since 1997, progressing from Lecturer to Professor in 2005. His research focuses on Fuzzy Logic, Control Systems, and their applications in biomedical and industrial contexts. Mahfouf leads the Intelligent Systems Research Laboratory and has contributed over 370 publications, including influential work on fuzzy modeling and predictive control. Education: Ing.Dipl. (Hons) in Control Systems MPhil in Control Systems (University of Sheffield, 1988) PhD in Control Systems (University of Sheffield, 1991) Research Interests: Fuzzy Logic applications, Artificial Intelligence, Neural Networks, Model-Based Predictive Control, Biomedical Engineering (e.g., ICU Decision Support Systems), and Manufacturing Systems (e.g., granulation processes, surface metrology). Key Achievements: Recipient of the IEE Hartree Premium Award (1992) and MEDIPEX Innovation Award. His work integrates fuzzy logic into real-time systems for aviation, healthcare, and robotics. Grants & Labs: Leads the Intelligent Systems Research Lab. Active in collaborative projects with industry (e.g., pharmaceuticals, aerospace). His research bridges theory and practice, emphasizing data-driven solutions for complex systems.

Thomas O'Brienمشاهده پروفایل

مدرس

• Para Sport Science
• Exercise Physiology
• Sports Performance Analysis
• +۴ مورد دیگر

Thomas O'Brien is a Lecturer in the School of Sport, Exercise and Health Sciences at Loughborough University, specializing in applied disability and para sport science. He holds a BSc in Sport and Exercise Science (Aberystwyth University, 2016), an MSc in Exercise Physiology (Loughborough University), and a PhD in thermoregulatory challenges in para populations (completed part-time at the Peter Harrison Centre for Disability Sport, PHC). His roles have included Research Assistant (2017), Performance Scientist for GB Wheelchair Rugby (2021–2024), and Research Associate at PHC (2022–present). He has been a key member of multidisciplinary teams supporting Paralympic athletes, focusing on performance optimization and physiological testing. His research interests center on para-sport biomechanics, thermoregulation in disabled athletes, and wheelchair rugby performance enhancement. He has collaborated with national governing bodies to advance knowledge exchange in wheelchair rugby and contributed to strategies for the Tokyo 2020 and Paris 2024 Paralympics. His work integrates sports science, strength and conditioning, and clinical physiology to improve athlete preparation and performance. Teaching roles include module leadership for the MSc Musculoskeletal Sport Science and Health’s “Para Sport: Rehabilitation to Performance” course and delivery of lectures on applied para-sport across undergraduate and postgraduate programs. His research spans topics like wheelchair propulsion biomechanics, cooling strategies for SCI athletes, and tendon adaptations in elite wheelchair athletes.

Hans-Martin Schwabمشاهده پروفایل

استادیار

• Biomedical Engineering
• Ultrasound Imaging
• Photoacoustics
• +۳ مورد دیگر

Hans-Martin Schwab is an Assistant Professor at the Biomedical Engineering Department of Eindhoven University of Technology (TU/e), affiliated with the Eindhoven MedTech Innovation Center and the Photoacoustics and Ultrasound Laboratory. His research focuses on advanced ultrasound and photoacoustic imaging techniques, including multi-perspective imaging, model-based artifact reduction, and deep learning-assisted signal processing. He contributes to UN Sustainable Development Goals through innovations in medical imaging technologies. His expertise spans transducer engineering, circumferential strain imaging, and ultrasound reconstruction methods. Recent collaborations involve international teams working on applications like vascular strain analysis and tissue regeneration using ultrasound phased arrays. Over 28 publications since 2020 highlight contributions to photoacoustic imaging systems, numerical simulations, and clinical ultrasound advancements. Prof. Schwab teaches courses on ultrasound imaging, clinical measurements, and blood oxygenation imaging. He has supervised numerous projects, including DBL initiatives on light/sound integration for medical imaging. His work emphasizes translating theoretical models into practical clinical tools, with a focus on improving diagnostic accuracy and patient outcomes.

Hongrui Jiangمشاهده پروفایل

استاد

• MicroElectroMechanical Systems (MEMS)
• BioMEMS
• Optical MEMS (MOEMS)
• +۷ مورد دیگر

Hongrui Jiang is a Professor in the Department of Electrical & Computer Engineering at the University of Wisconsin-Madison, with affiliations in Biomedical Engineering and Materials Science & Engineering. His research focuses on MEMS, BioMEMS, optical systems, microfluidics, and biomimetics, with applications in biomedical imaging, wearable sensors, and smart materials. He holds the Lynn H. Matthias Professorship and has been recognized as an IEEE Fellow and AIMBE Fellow. Education: PhD 2001, Cornell University MS 1999, Cornell University BS 1995, Peking University (Summa cum Laude) Research Interests: Jiang’s work spans micro-optical imaging systems, biological/chemical sensing, functional polymers, and bioinspired design. Key areas include wearable gait monitoring, laparoscopic surgery visualization, and tunable optical devices. His lab develops novel MEMS-based sensors and actuators for biomedical and environmental applications. Publications: Recent work emphasizes real-time video stabilization, 3D medical imaging systems, and wearable sensor networks. Themes include fusion of computer vision with biomedical engineering and optimization of microsystems for clinical use. Awards: 2023 Bryon Bird Award 2017 IEEE Fellow 2014 Lynn H. Matthias Professorship 2011 NIH New Innovator Award Teaching & Grants: Courses include E C E 230 (Circuit Analysis) and advanced graduate research seminars. His grants support projects in MEMS fabrication, biomedical sensors, and smart materials. He advises graduate students in ECE and collaborates across disciplines. Labs/Teams: Leads the Jiang Lab for MEMS and BioMEMS research, focusing on translational technologies for healthcare and engineering challenges.

Wei Wuمشاهده پروفایل

استاد

• Clinical Psychology
• Quantitative Psychology
• Longitudinal Data Analysis
• +۳ مورد دیگر

Wei Wu is a Professor in the Department of Psychology at Indiana University, specializing in quantitative methodology for psychological research. His work bridges clinical applications with advanced statistical techniques, particularly in longitudinal study designs. His academic credentials include: Ph.D. in Quantitative Psychology from Arizona State University (2008) M.S. in Personality Psychology from East China Normal University (2003) B.Ed. in Special Education from East China Normal University (1997) Professor Wu's research focuses on developing and refining statistical methods for psychological inquiry, with emphasis on missing data handling, growth curve modeling, and model fit evaluation. His clinical psychology background informs applications in educational and developmental contexts, particularly regarding grade retention effects. Methodological rigor defines his contributions, advancing best practices for longitudinal data analysis in social sciences. Analysis of his publication record reveals consistent innovation in missing data techniques and experimental design optimization. His work demonstrates increasing sophistication in planned missing data approaches, with growing emphasis on computational efficiency and real-world applicability across psychology subfields. Recent publications integrate SEM and multilevel frameworks to solve complex modeling challenges. Professor Wu has secured substantial research funding through competitive grants: NSF grant ($422,900, 2011-2016) as PI for planned missing data designs in longitudinal research NIH grant ($1.7M, 2010-2014) as Statistical Consultant for resilience determinants study PCORI grant ($1.5M, 2013-2016) as Statistical Consultant for burnout impact research New Faculty General Fund ($8,000, 2010-2013) for model fit evaluation methods NICHD grant (2004-2008) as Research Assistant for grade retention study He actively mentors graduate researchers, with documented advisees including A. I. Carroll, P-Y. Chen, F. Jia, R. Kinai, F. Gu, P. R. Miller, and P. Pornprasermanit across multiple publications. While no formal lab structure is specified, his extensive grant collaborations indicate active participation in interdisciplinary research networks focused on methodological advancement.

• Multimodal Deep Learning
• Clinical Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Dr. Ruchit Agrawal is an Assistant Professor of Computer Science and Head of Computer Science Outreach at the University of Birmingham Dubai. Previously, he served as a Postdoctoral Researcher in AI for Healthcare at the University of Oxford’s Computational Health Informatics Lab, and as a Marie Curie AI Researcher in the transnational MIP-Frontiers project at Queen Mary University of London. His work focuses on optimizing healthcare systems using Machine Learning, alongside contributions to Natural Language Processing, Audio Signal Processing, and Multimodal Deep Learning. He holds a PhD in Computer Science from Queen Mary University of London and an MS by Research from IIIT Hyderabad. Education: PhD in Computer Science (Queen Mary University of London, 2021) MS by Research in Machine Translation (IIIT Hyderabad, 2017) Research Interests: Clinical Machine Learning for healthcare system optimization Natural Language Processing with a focus on Indian languages and context-aware models Audio Signal Processing for music performance analysis and stuttering detection Development of multimodal deep learning frameworks for diverse applications Adaptive AI systems leveraging contextual and positional encoding techniques Publications highlight trends in healthcare AI, multilingual NLP, and audio-visual alignment. Recent work includes Arabic sentiment analysis, stuttering detection via MMSD-Net, and stock price prediction using FB-GAN. Earlier contributions address structure-aware synchronization in music performance data and transformer-based post-editing for low-resource languages. His research bridges theoretical advancements with practical implementations in clinical, financial, and cross-modal domains. Scientific awards include the prestigious Marie Skłodowska-Curie scholarship (2017–2020) supporting his deep learning research in audio signal processing. Advising and grants: While no formal advisees are listed, his roles involve leading outreach initiatives and guiding collaborative projects at the Computational Health Informatics Lab during his postdoctoral tenure. Labs/Teams: Active member of the Computational Health Informatics Lab (Oxford) and Machine Translation group at FBK (Italy). His work also intersects with the MIP-Frontiers transnational research project.

Anna Spagnolliمشاهده پروفایل

استاد

• Usable Privacy
• Technology Acceptability
• Human-Computer Interaction
• +۷ مورد دیگر

Anna Spagnolli is a Full Professor at the University of Padua, specializing in Human-Computer Interaction (HCI), Usable Privacy, and Technology-Mediated Interaction. Her research focuses on ethical AI, IoT systems, virtual reality (VR) applications in prosocial behavior, and digital health assistants. She has led projects like SAFE GYMS (IoT for safety in sports/work environments) and DOMHO (IoT for co-housing). Her work bridges technology design with user needs, emphasizing transparency, accessibility, and ethical considerations. Research Interests: Usable Privacy and Security AI Ethics and User-Centered Design Virtual Reality for Behavioral Training IoT in Healthcare and Safety Qualitative Methods in HCI Key Contributions: Developed frameworks for transparency in healthcare AI and digital assistants. Explored prosocial behavior through VR simulations of emergencies. Designed inclusive mobile apps (e.g., SAFE TALK) for social support during isolation. Her publications span over two decades, addressing topics from sustainable technology to workplace ergonomics with collaborative robots. Despite no listed awards, her extensive academic output reflects significant influence in HCI and technology ethics.

Anish Sevekariمشاهده پروفایل

پژوهشگر

• Machine Learning
• Algorithms
• Optimization
• +۳ مورد دیگر

Anish Sevekari is a Postdoctoral Associate at the University of Pittsburgh. His research focuses on machine learning, algorithms, optimization, and theoretical computer science. He investigates topics such as neural network training dynamics, generative models, algorithmic analysis beyond worst-case scenarios, and efficient inference techniques. His work bridges theoretical foundations with practical applications in areas like probabilistic modeling and combinatorial optimization. Key research interests include normalizing flows, ensemble methods, score-based learning, stochastic optimization, and combinatorial algorithms. His recent publications explore acceleration of NCE convergence, progressive ensemble distillation, and provable benefits of score matching. He has published extensively in top-tier venues, with a focus on theoretical guarantees and practical efficiency. His research trends emphasize bridging gaps between machine learning and traditional algorithmic analysis, particularly in probabilistic frameworks and high-dimensional data problems. No scientific awards or grants are explicitly mentioned in the provided information.

Jichun Liمشاهده پروفایل

استاد

• Mathematical Modeling
• Scientific Computing
• Numerical Analysis
• +۴ مورد دیگر

Jichun Li is a Professor in the Department of Mathematical Sciences at the University of Nevada, Las Vegas. His research focuses on mathematical modeling, scientific computing, and numerical analysis with applications to electromagnetism and metamaterials. He specializes in developing advanced numerical methods like Finite Element Methods (FEM), Discontinuous Galerkin (DG), and Finite-Difference Time-Domain (FDTD) schemes for simulating wave propagation in complex media such as metamaterials and photonic crystals. Key research areas include metamaterials (e.g., hyperbolic metamaterials, cloaking devices), electromagnetic wave manipulation (e.g., surface plasmon polaritons on graphene), and computational techniques for solving Maxwell’s equations in dispersive and nonlinear media. His work addresses challenges like numerical stability, superconvergence analysis, and implementation of Perfectly Matched Layers (PML) for absorbing boundary conditions. Recent efforts involve integrating deep learning with numerical PDE methods for financial applications and advancing time-domain simulations for metamaterial cloaking and optical black holes. He has contributed to software tools like MATLAB-based edge element codes for metamaterial modeling. His research bridges theoretical analysis and practical simulation, with applications in photonics, quantum optics, and computational electromagnetics. Notable achievements include over 150 peer-reviewed articles, methodological innovations in numerical electromagnetics, and active participation in interdisciplinary collaborations at the Center for Applied Math & Statistics. His work emphasizes high-order methods and rigorous mathematical validation for engineering and scientific problems.

Si Liمشاهده پروفایل

استاد

• Corporate Finance
• Labor Economics
• Bankruptcy Studies
• +۳ مورد دیگر

Si Li is Professor of Finance and Director of the Financial Services Research Centre at Wilfrid Laurier University's Lazaridis School of Business and Economics. His research examines corporate bankruptcy impacts, labor market dynamics, fintech, and financial regulation using advanced econometric methods. With over 30 publications, Li's work explores: Employee costs during corporate restructuring Fintech's impact on inequality Managerial compensation trends Market risk factors He teaches finance courses and maintains active industry collaborations through the Research Centre.